Deep Neural-Gas Clustering for Instance Segmentation across Imaging Experiments


CNNs are characterized in particular by the ability to independently learn suitable features from a given data set. However, the resulting latent space is optimized for the given training data. Especially for tasks that require a high generalization ability, like e.g. the segmentation of single cells in a microscopic image across various experiments, these specific solutions might not offer optimal results. In this work, we improve generalization with an additional unsupervised training step that operates in the latent space. First experiments with the Kaggle cell segmentation competition data show a strong improvement in the generalization of acquired knowledge when using a soft- and hard-competitive Neural-Gas algorithm for deep clustering with a standard CNN architecture.

Original languageEnglish
Title of host publication2020 International Joint Conference on Neural Networks (IJCNN)
Publication date07.2020
Article number9207602
ISBN (Print)978-1-7281-6926-2
ISBN (Electronic)978-1-7281-6927-9
Publication statusPublished - 07.2020
Event2020 International Joint Conference on Neural Networks - Virtual, Glasgow, United Kingdom
Duration: 19.07.202024.07.2020
Conference number: 163566

Research Areas and Centers

  • Research Area: Intelligent Systems
  • Centers: Center for Artificial Intelligence Luebeck (ZKIL)

DFG Research Classification Scheme

  • 409-05 Interactive and Intelligent Systems, Image and Language Processing, Computer Graphics and Visualisation


Dive into the research topics of 'Deep Neural-Gas Clustering for Instance Segmentation across Imaging Experiments'. Together they form a unique fingerprint.

Cite this